Analog and radio frequency (RF) system-level simulation using frequency relaxation

ABSTRACT

Analog and radio frequency system-level simulation using frequency relaxation. Embodiments of the invention use a frequency relaxation approach for analog/RF system-level simulation that accommodates both large system size and complex signal space. The simulator can determine an output response for a system by partitioning the system into blocks and simulating the propagation of an input signal through the blocks. The input signal can take various forms, including a multi-tone sinusoidal signal, a continuous spectra signal, and/or a stochastic signal. Frequency relaxation is applied to produce individual responses. The output response can be computed based on obtaining convergence of the individual responses. The input to embodiments of the simulator can be a circuit netlist, or a block-level macromodel.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from commonly owned, provisional patentapplication Ser. No. 60/604,278, filed Aug. 25, 2004, the entiredisclosure of which is incorporated herein by reference.

BACKGROUND

The topic of analog circuit simulation has been extensively studied eversince the advent of integrated circuits over three decades ago.Recently, the remarkable evolution of the wireless/personal electronicsmarket has introduced numerous new analog/RF products, as well as newchallenges for the simulation of such systems. In order to conquer theincreasing difficulties encountered in IC simulation, many advancedtechniques; including steady-state analysis and envelope following havebeen developed. At the same time, the advance of very large scaleintegration (VLSI) technologies has made it possible to integrate anentire mixed-signal system onto a single chip or within a singleelectronic package. It is, therefore, important to evaluate theperformance of the full mixed-signal system during both top-down designand bottom-up verification.

As IC technologies scale to finer feature sizes and circuit applicationsmove to higher frequency bands, the behavior of analog/RF circuitsbecomes more complicated and more difficult to understand. Although onlya small section of the entire mixed-signal system operates with trulyanalog signals, the design and verification of the analog components isgenerally the most challenging. Furthermore, design specifications arenot only defined for individual analog/RF circuit blocks, but detailedhigh-level specifications are described for the entire analog/RFsubsystem. For example, an analog front-end in the wireless transceiveris evaluated by several system-level specifications such as ACPR(adjacent channel power ratio). Such specifications require that theanalog/RF subsystem is verified independently, as an intermediate stagebetween circuit-level analysis and mixed-signal system-level simulation.

An example of a simulator that applies circuit-level analysis to acircuit is shown in FIG. 1. Simulator 100 accepts a circuit descriptionthat is obtained at process box 102, and produces the output response atprocess box 104. Such a simulator works by first building equations thatdescribe the overall circuit at process box 106. Often, such a simulatoruses modified nodal analysis (MNA) to produce the equations. Theseequations, which can be both linear and nonlinear, are solved at processbox 108.

Unfortunately, directly applying or extending existing simulationtechniques to analog/RF system-level analysis suffers from seriouslimitations. For example, a complete analog/RF system consists of alarge number of individual analog circuit blocks. As the system sizeincreases, the traditional algorithms for circuit-level simulation donot accommodate the system-level simulation requirements. Additionally,time-domain transient analysis is effective for analog/digitalco-simulation. However, for an analog/RF system, the wide-bandinput/output signals (e.g. the power spectral density for random noise)are best described by frequency-domain representations, as analyzing ananalog/RF system in the time-domain over wide frequency bands canquickly become infeasible.

SUMMARY

Embodiments of the present invention use a frequency relaxation approachfor analog/RF system-level simulation that accommodates both largesystem size and complex signal space. The simulator disclosed in theexample embodiments herein can capture various second order effects(e.g. nonlinearity, noise, etc.) for both time-invariant andtime-varying (e.g. switching mixer) systems. The simulator operates inthe frequency domain and supports wide-band analog input (deterministic)signals (e.g. multi-tone sinusoidal signals) as well as wide-band noise(stochastic) signals. The simulation methodology can include acombination of macromodeling, partitioning, and frequency relaxation.

In example embodiments, the simulator can determine an output responsefor a system by partitioning the system into a plurality of blocks andsimulating the propagation of an input signal through each of theplurality of blocks to produce a description of each of the plurality ofblocks. Frequency relaxation is applied to the description for each ofthe plurality of blocks to produce a plurality of individual responses,at least one for each of the plurality of blocks. The output response iscomputed based on obtaining convergence of the individual responses.

The input to the simulator of example embodiments of the invention canbe a circuit netlist, or a block-level macromodel or their combinations.In at least some embodiments, simulation cost can be reduced byobtaining a block-level macromodel of the analog system, andpartitioning the system using the block-level macromodel. Additionally,the total contribution of all noise sources in the system can berepresented by injecting noise into the block-level macromodel. Theinput signal propagated through the various blocks of the system cantake various forms, including a multi-tone sinusoidal signal, acontinuous spectra signal, and/or a stochastic signal.

In some embodiments, the processes and/or sub-processes of the inventioncan be carried out with the aid of an instruction execution orprocessing platform. For example, a partitioning sub-process, an MNAsub-process, a frequency relaxation sub-process, and a convergencesub-process can be implemented as functional blocks of instructionsexecuting within the processing platform. In such an embodiment, theplatform in conjunction with a computer program product includingcomputer program instructions can form the means to carry out at leastsome portions of the processes of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the operation of an example circuit simulator thatdoes not employ an embodiment of the present invention.

FIG. 2 is a functional block and process flow diagram illustrating theoperation of a circuit simulator according to example embodiments of thepresent invention.

FIG. 3 is an example macromodel of the type that can find use with anembodiment of the present invention.

FIG. 4 is a flowchart illustrating a portion of the process of anexample embodiment of the present invention.

FIG. 5 is a flowchart illustrating at least a portion of the process ofan example embodiment of the present invention, wherein a multi-tonesinusoidal signal is used.

FIG. 6 is a flowchart illustrating at least a portion of the process ofan example embodiment of the present invention, wherein a continuousspectra signal is used.

FIG. 7 is a flowchart illustrating at least a portion of the process ofan example embodiment of the present invention, wherein a stochasticsignal is used.

FIG. 8 is a block diagram of an instruction execution system being usedto implement an example embodiment of the invention.

FIG. 9 is a block diagram of an example analog system to which anembodiment of the invention can be applied.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENT(S)

The present invention will now be described in terms of specific,example embodiments. It is to be understood that the invention is notlimited to the example embodiments disclosed. It should also beunderstood that not every feature of the devices or sub-process of themethods described are necessary to implement the invention as claimed inany particular one of the appended claims. Various elements, steps,processes, and features of various embodiments of devices and processesare described in order to fully enable the invention. It should also beunderstood that throughout this disclosure, where a process or method isshown or described, the steps of the method may be performed in anyorder or simultaneously, unless it is clear from the context that onestep depends on another being performed first.

FIG. 2 presents a functional block and process diagram showing anoverview of example embodiments of the present invention. Relaxationsimulator 200 accepts as input either a transistor-level netlist or ablock-level macromodel, or their combinations, which is obtained inprocess box 202. Simulator 200 produces an output response for theanalog system at process box 204. Within simulator 200, the analogcircuit or system is partitioned into relatively small blocks at box206. The sub-processes of boxes 208 and 210 are then performed on ablock-by-block basis, as indicated by the multiple process paths withthese two process boxes and the ellipsis dots shown in FIG. 2.

Still referring to FIG. 2, at box 208, in this example embodiment, asimulated signal is propagated through the appropriate circuit block anda mathematical description of that circuit block is derived based on thepropagation of the signal. In at least some embodiments, thisdescription consists of the circuit equation or circuit equations forthe block that are derived using modified nodal analysis (MNA). MNA is aknown way of generating circuit equations that involves deriving oneequation for each node not attached to a voltage source and youaugmenting these equations with an equation for each voltage source.Frequency relaxation is than applied to the description of the block atbox 210. In example embodiments, the sub-process of applying thefrequency relaxation technique allows the equations to be solved,resulting in a calculated, individual output response for each block.Convergence is tested at process box 212 of FIG. 2, and when theindividual output responses converge, the total output response isproduced at block 204.

As mentioned above, a simulator according to embodiments of theinvention can accept as input, either a transistor-level netlist, or ablock-level macromodel, or their combinations. In at least some cases,the use of macromodeling can improve computational efficiency becausecomputational complexity is reduced. A discussion of latency andmacromodeling relative to analog system simulation may help the readerto fully appreciate how macromodeling can be of benefit.

Circuit blocks/components, including those that are analog, aregenerally defined as part of a top-down design methodology. The blocksare designed to be weakly coupled to provide for their independentspecification and creation. For analog/RF components there is also adominant signal flow or propagation direction, which, along with theweak coupling, allows system designers to analyze them using astate-flow type of model.

For example, in a receiver front-end, the RF signal propagates throughthe low noise amplifier (LNA), mixer, intermediate frequency (IF)amplifier, etc. By proper design, the parasitic coupling between thesecomponents is restricted to ensure that each component operatescorrectly. It follows that any backward signal propagation due to secondorder effects (e.g. nonideal coupling) is much weaker than the forwardpropagation. For such simulation models that are characterized bydominant unidirectional signal flow and blocks with high latency,well-known relaxation methods can be applied to exploit theseproperties. Namely, if the circuit blocks are solved individually in aproper order, a good approximate solution to the entire system isquickly produced after several iterations.

However, in at least some cases, it is not sufficient to explore thelatency only among circuit blocks. For numerical simulation, computationcost is determined by the circuit size, as well as the complexity of thesignal space for representing the circuit response. An importantdifference between circuit-level analysis and system-level simulation isthat, in system-level simulation, the response signal space is muchlarger. For example, a wireless transceiver front-end is tested withdigitally modulated signals that contain a large number of frequencycomponents and that spread over various (RF, IF and base) frequencybands when passing through the entire transceiver. Such a large signalspace has to be completely considered during the simulation of eachcircuit block. Applying a relaxation approach facilitates thepartitioning of a large system into small blocks but, unfortunately, itcannot decompose the signal-response space simultaneously. Therefore, insystem-level analysis, it is inefficient, if not impossible, to simulateeach circuit block by traditional circuit-level techniques.

A purpose of macromodeling is to extract simple, high-level abstractionsthat facilitate fast evaluation of nonideal effects in analog/RFcircuits. However, from the relaxation point of view, the macromodelingprocess can also help to break the strong feedback loops inside acircuit that may preclude decomposing the circuit into smaller units. Inanalog/RF circuit design, feedback techniques are widely used in orderto improve the circuit performance. These strong feedback loops aresolved during the macromodeling process and the final macromodel canincorporate the closed-loop input-output relation in an explicit form.After macromodeling, a circuit block is further partitioned into muchsmaller units (e.g. static nonlinear functions and linear transferfunctions in macromodels, which can facilitate efficient system levelsimulation.

Shown in FIG. 3 is a typical circuit block macromodel, 300. Amacromodeling algorithm approximates the circuit input-output relationby a number of static nonlinear functions, for example, x², and x³, aswell as linear transfer functions, F, H_(P), H₁, H₂ and H₃. Thesefunctions serve to decompose the entire circuit into much smaller units.The static nonlinear functions and linear transfer functions in FIG. 3are determined by the circuit design of the current stage, the outputimpedance of the previous stage and the input impedance of the nextstage. Due to a variety of nonidealities, signals in a circuit blockmight not propagate from input to output in an exactly unique direction.The reverse gain is nonzero and should be modeled by the backward signalpath, between nodes 301 and 302 as shown in FIG. 3. In addition, with atleast some embodiments of the invention, a noise source would beinjected at the circuit output, which represents the total contributionof all noise sources (white noise, shot noise, etc.) inside the circuit.Such a noise source can be extracted by macromodeling.

It should be noted that analog/RF systems often include time-varyingcomponents such as switching-mixers. These time-varying components bringabout several different features. First, the linear transfer functionsof the macromodel are not restricted to traditional time-invariant ones.Linear periodically time-varying (LPTV) transfer functions should beapplied to describe the input-output behaviors for time-varying circuitsaccording to:

${{H\left( {t,{j\;\omega}} \right)} = {\sum\limits_{n = {- \infty}}^{\infty}{{H_{n}\left( {j\;\omega} \right)} \cdot {\mathbb{e}}^{j\; n\;\omega_{0}t}}}},$where ω₀=2π/T and T is the period of the LPTV transfer function.Secondly, due to the time-varying effects, the noise source in FIG. 3 isa cyclostationary process and, therefore, should be characterized by aset of harmonic power spectral densities.

FIG. 4 is a flowchart that illustrates how an analog circuit or systemcan be partitioned according to example embodiments of the invention.Like most flowcharts, FIG. 4 as well as the other flowcharts presentedherein illustrate processes as a series of process or sub-process boxes.Process 400 starts from process box 402, where a given analog/RF systemis converted to a signal flow graph; i.e. a weighted directed graphG=(V,E) without multi-edges and self-loops so that E contains all edges.Each vertex V_(i)εV denotes an input, output or internal node, each edgeE_(i)=[V_(m),V_(n)]εE denotes a signal path from vertex V_(m) to V_(n),and the weight of E_(i) stands for the static nonlinear function, lineartransfer function or their combinations associated with the signal path.

Still referring to FIG. 4, at box 404 of FIG. 4, the graph ispartitioned into subsets E_(F) and E_(B) such that, E_(F)∩E_(B)=Φ,E_(F)∪E_(B)=E and both G_(F)=(V,E_(F)) and G_(B)=(V,E_(B)) are directedacyclic graphs. It is well known that scheduling the partitioning of thesignal flow graph in a proper order can speed up the convergence of arelaxation (e.g. Gauss-Seidel) iteration. Signal flows in an analog/RFsystem tend to propagate in a unique direction. If the system is solvedalong the same direction in which signals propagate, a good approximatesolution can be reached quickly.

After scheduling, an ordered sequence of vertices {V₁,V₂, . . . ,V_(N)}is obtained at box 406. Either a Gauss-Jacobi or Gauss-Seidel iterationcan then be applied for relaxation simulation. In example embodiments,the Gauss-Seidel algorithm is implemented.

FIGS. 5, 6, and 7 are flowcharts which describe the propagation,frequency relaxation, and convergence aspects of three exampleembodiments of the simulator. The signal propagated through an analog/RFsystem for purposes of these embodiments can be classified into twocategories: a deterministic signal (e.g. multi-tone sinusoidal signal)and a stochastic signal (e.g. random noise). FIGS. 5 and 6 describe theprocess using propagation of deterministic signals.

In the embodiment in the figures, using the macromodel circuit of FIG. 3as an example, the relaxation simulator first injects the input signalat node 301. After that, the input signal propagates through threedifferent paths {H₁}, {H_(P)→x²→H₂} and {H_(P)→x³→H₃}, and all signalsfrom these three paths add together at node 302. Next, the signal atnode 302 returns node 301 through the backward path {F} and the signalvalue is further changed at node 301. The relaxation simulator ofembodiments of the invention propagates these signals back and forthinside the system. Eventually, if the reverse gain, F, is sufficientlysmall, all signals in the system become stable after several iterations.The relaxation simulation converges to the solution of the actual systemresponse. As part of the signal propagation, the individual outputsignals when the input signals pass through basic macromodeling unitsneed to be computed, i.e. static nonlinear functions and linear transferfunctions.

FIG. 5 illustrates the process with a multi-tone sinusoidal signal. Amulti-tone signal is the sum of a number of sinusoidal terms:

${{x(t)} = {\frac{1}{2} \cdot {\sum\limits_{i = 1}^{M}\left( {{A_{i}\;{\mathbb{e}}^{j\;\omega_{i}t}} + {A_{i}^{*}\;{\mathbb{e}}^{{- j}\;\omega_{i}t}}} \right)}}},$where ω_(i)>0 and A_(i)* denotes the conjugate of A_(i). When themulti-tone signal passes through the static nonlinear block x^(K), itsoutput response contains various terms of the form:

$\begin{matrix}{{\prod\limits_{i = 1}^{M}{C_{{ai},{bi}} \cdot \left( A_{i} \right)^{ai} \cdot \left( A_{i}^{*} \right)^{bi} \cdot {\mathbb{e}}^{{j{({{ai} - {bi}})}}\omega_{i}t}}},} \\{{where}\text{:}} \\\begin{matrix}{{\sum\limits_{i = 1}^{M}\left( {{ai} + {bi}} \right)} = K} & \; & {\left( {{{ai} \geq 0},{{bi} \geq 0}} \right).}\end{matrix}\end{matrix}$The coefficient C_(ai,bi) can be computed using combinatorics.

Given a multi-tone input, the output response y(t) of the LPTV transferfunction is the sum of the individual responses to all sinusoidal tones,i.e.

${y(t)} = {\frac{1}{2}{\sum\limits_{i = 1}^{M}{\sum\limits_{n = {- \infty}}^{\infty}{\begin{bmatrix}{{A_{i}{H_{n}\left( {j\;\omega_{i}} \right)}\;{\mathbb{e}}^{{j{({\omega_{i} + {n\;\omega_{0}}})}}t}} +} \\{A_{i}^{*}{H_{n}\left( {{- j}\;\omega_{i}} \right)}\;{\mathbb{e}}^{{j{({{- \omega_{i}} + {n\;\omega_{0}}})}}t}}\end{bmatrix}.}}}}$Thus, process 500 of FIG. 5 begins at box 502, where X_(i) ^(k)(t) isset to zero and k=0. k is incremented at box 504, and i set so that i=1at box 506.

Still referring to FIG. 5, at box 508 of FIG. 5, the function X_(i)^(k)(t) is computed at the ith vertex, where the function has the form:X _(i) ^(k)(t)=0.5└A _(i1) ^(k) e ^(jw) ¹ ^(t) +A _(i1) ^(k) *e ^(−jw) ¹^(t)+ . . . ┘.At box 510 i is incremented. At box 512, if i has not reached N,processing proceeds to box 514. Otherwise, the computation at box 508 isrepeated. At box 514, the truth of the expression:∥X ^(k) −X ^(k−1)∥≦ε,is evaluated. If this condition is met, convergence has been achieved,and processing stops at box 516, otherwise, processing returns to box504.

FIG. 6 illustrates a process, 600, that is analogous to the process ofFIG. 5, except that in this case, a continuous spectra signal is used. Adigitally modulated signal has continuous frequency spectra X(jω). WhenX(jω) passes through the static nonlinear block x^(K), its outputresponse Y(jω) equals the convolution of X(jω) in frequency domain asshown by:

${Y\left( {j\;\omega} \right)} = {\left( \frac{1}{2\;\pi} \right)^{K - 1} \cdot {\underset{\underset{K}{︸}}{{X\left( {j\;\omega} \right)} \otimes {X\left( {j\;\omega} \right)} \otimes \ldots \otimes {X\left( {j\;\omega} \right)}}.}}$Otherwise, if X(jω) passes through the LPTV transfer function, theoutput response Y(jω) can be expressed as:

${Y\left( {j\;\omega} \right)} = {\sum\limits_{n = {- \infty}}^{\infty}{\left\lbrack {{H_{n}\left( {{j\;\omega} - {j\; n\;\omega_{0}}} \right)}\;{X\left( {{j\;\omega} - {j\; n\;\omega_{0}}} \right)}} \right\rbrack.}}$Thus, still referring to FIG. 6, at box 602, X_(i) ^(k)(t) is set tozero and k=0. k is incremented at box 604, and i is set to 1 at box 606.At box 608, the response X_(i) ^(k) where X_(i) ^(k)(w) is a continuousspectra signal, that is, a continuous function of the frequency ω. Atbox 610 i is incremented. At box 612, if i has not reached N, processingproceeds to box 614. Otherwise, the computation at box 608 is repeated.At box 614, the truth of the expression:∥X ^(k) −X ^(k−1)∥≦ε,is evaluated. If this condition is met, convergence has been achieved,and processing stops at box 616, otherwise, processing returns to box604.

As noted previously, either deterministic or stochastic signals can bepropagated through the system according to example embodiments of theinvention. Due to the time-varying effects in today's analog/RF systems,random noise is described as a cyclostationary stochastic process whosepower spectral density is a time-varying function:

${{X\left( {t,{j\;\omega}} \right)} = {\sum\limits_{n = {- \infty}}^{\infty}\left\lbrack {{X_{n}\left( {j\;\omega} \right)} \cdot {\mathbb{e}}^{j\; n\;\omega_{0}t}} \right\rbrack}},$where X_(n)(jω) is the nth harmonic power spectral density of X.

There are differences between deterministic signals and stochasticsignals. First, since the amplitude of the physical noise signal is verysmall, nonlinearities can be ignored for noise analysis. For purposes ofthe embodiments described herein, only linear transfer functions areconsidered when studying the propagation of random noise. Secondly, whentwo stochastic processes A and B are added, their power spectraldensities can be added if and only if A and B are uncorrelated. Takingthe circuit block of FIG. 3 again as an example, the stochastic noise atnode 302 propagates to node 301 through the path {F} and then returnsnode 302 through {H₁}. But, at node 302, the original noise signal andthe returning noise signal cannot be added directly, since they comefrom the same source, thereby making them correlated. At least partlyfor this reason, process 700 of FIG. 7 is used when a noise signal ispropagated for the relaxation technique. At box 702 from a system with Mindependent noise sources X_(i) where i=1,2, . . . ,M. At box 704, i isset equal to 1. At box 706, H_(j) ⁰ as well as k are both set to 0. k isincremented at box 708 and j is set equal to 1 at box 710.

Still referring to FIG. 7, note that instead of propagating the noisesignal directly, box 712 “propagates” the transfer function from eachnoise source to the system output. When several transfer functions areparallel-connected at one node, the overall transfer function is equalto the sum of all individual ones. This implies the fact that transferfunctions can be handled as response signals and propagated throughoutthe system during relaxation iteration. Taking the circuit in FIG. 3 asan example, the transfer function is first initialized from noise sourceto node 302 as H_(Noise→2)=1. Then, H_(Noise→2) cascades with F andpropagates to node 301 which yields H_(Noise→i)=F. Next, H_(Noise→1)cascades with H₁, returns node 302 and further changes the value ofH_(Noise→2). Note that one can ignore the nonlinear signal paths{H_(P)→x²→H₂} and {H_(P)→x³→H₃} here. The relaxation simulatorrepeatedly applies these propagations until the closed-loop transferfunctions are obtained from the input to each node of the system. In box712, H_(j) ^(k) is computed at the jth vertex, where H_(j) ^(k) is theclosed-loop transfer function from the noise source X_(j) to the jthvertex.

The above discussion shows that the cascading of different transferfunctions can be used with the noise simulation. The following equationgives the overall transfer function H(t,jω) when two LPTV transferfunctions F(t,jω) and G(t,jω) are cascaded.

${H\left( {t,{j\;\omega}} \right)} = {\sum\limits_{n = {- \infty}}^{\infty}{\left\{ {\sum\limits_{k = {- \infty}}^{\infty}{\left\lbrack {{G_{n - k}\left( {{j\;\omega} + {j\; k\;\omega_{0}}} \right)}\;{F_{k}\left( {j\;\omega} \right)}} \right\rbrack \cdot {\mathbb{e}}^{j\; n\;\omega_{0}t}}} \right\}.}}$

After the transfer function H(t,jω) from noise source X(t,jω) to theoutput is obtained, the nth harmonic power spectral density Y_(n)(jω) atthe output can be expressed as a function of the input harmonic powerspectral densities X_(k)(jω).

${Y_{n}\left( {j\;\omega} \right)} = {\sum\limits_{m = {- \infty}}^{\infty}{\sum\limits_{k = {- \infty}}^{\infty}{\begin{Bmatrix}{{H_{m}\left( {{{- j}\;\omega} - {j\; m\;\omega_{0}}} \right)} \cdot {X_{k}\left( {{j\;\omega} + {j\; m\;\omega_{0}}} \right)} \cdot} \\{X_{n - m - k}\left( {{j\;\omega} + {j\; m\;\omega_{0}} + {j\; k\;\omega_{0}}} \right)}\end{Bmatrix}.}}}$

Studying this equation, one would find that the input noise componentsat various frequencies {ω+mω₀; m= . . . ,−1,0,1, . . . } will mix to theoutput at frequency ω. In addition, the kth input harmonic componentX_(k)(jω) will translate to the nth output harmonic component Y_(n)(jω)These two features are differences between time-varying andtime-invariant systems. Compared with the traditional noise simulationapproach for LTI systems, the relaxation simulator described herein iscapable of accommodating such a noise folding effect involved in manymodern analog/RF systems.

Continuing through FIG. 7, at box 714, j is incremented. At box 716, ifj has not reached N, processing proceeds to box 718. Otherwise, thecomputation at box 712 is repeated. At box 718, the truth of theexpression:∥H ^(k) −H ^(k−1)∥≦ε,is evaluated. If the condition is not met, processing returns to box708. If the condition is met, processing proceeds to box 720, where theoutput noise Y_(i) is computed based on H and X_(i). At box 722, i isincremented. At box 724, the condition of i being less than or equal toM is checked. If the condition is met, processing branches back to box706. Otherwise, convergence has been achieved, and the response,Y=Y₁+Y₂+ . . . +Y_(M) is computed at box 726.

The convergence sub-process of embodiments of the invention can bereadily understood by considering the fact that, without loss ofgenerality, the system equation of a signal flow graph can be writtenas:X=HX+W,where X_(i) is the response signal at vertex V_(i), W_(i) is the inputsignal to V_(i), and H_(ij) is the operator associated with edge∩V_(j),V_(i)∪. Note that the diagonal elements in matrix H are 0, i.e.H_(ii)=0, since it can be assumed that there are no self-loops in thesignal flow graph.

Note that, as discussed with respect to partitioning, after scheduling,an ordered sequence of vertices {V₁,V₂, . . . ,V_(N)} is obtained.Either the Gauss-Jacobi or Gauss-Seidel iteration can be applied forrelaxation simulation. As previously mentioned, the Gauss-Seidelalgorithm is implemented in the example embodiments presented herein,since Gauss-Seidel iteration is more efficient (converges more quickly)than the Gauss-Jacobi approach with these embodiments.

In order to study the convergence property of the Gauss-Seideliteration, we partition the operator matrix H into:H=L+U,where L is a strictly lower triangular operator matrix corresponding tothe backward signal paths and U is a strictly upper triangular operatormatrix associated with the forward signal paths.

Given a system described by the above equations and operators L and Udefined above, it can be shown that the Gauss-Jacobi iteration error isbounded by:|X ^(k+1) −X*|≦|H|·|X ^(k) −X*|,and the Gauss-Seidel iteration error is bounded by:|X ^(k+1) −X*|≦(I−|L|)⁻¹ ·|U|·|X ^(k) −X*|.

In the equations above, I is the identity matrix and X* is the exactsolution of the system response. The notation |A| denotes avector/matrix whose elements correspond to the norms of the elements inA, i.e. |A|_(ij)=|A_(ij)|. It is easy to verify that |H|=|L|+|U| and |H|is a nonnegative matrix, i.e. all elements in |H| are nonnegative.

Based on the above, the convergence conditions for the Gauss-Jacobi andthe Gauss-Seidel iterations are ρ{|H|}<1 and ρ{(I−|L|)⁻¹·|U|}<1respectively, where ρ{A} stands for the spectral radius of matrix A. Inorder to further compare the convergence for these two iterationschemes, one additional theorem is needed from matrix analysis.

The Stein-Rosenberg Theorem states that if |H|=|L|+|U| is a nonnegativematrix with zero diagonal entries, and if the spectral radius ρ{|H|}<1,then ρ{(I−|L|)⁻¹·|U|}<ρ{|H|}<1. The Stein-Rosenberg theorem suggests animportant fact, that since there are no self-loops in the signal flowgraph, i.e. H_(ii)=0, the Gauss-Seidel iteration is more efficient(converges more quickly) than the Gauss-Jacobi approach in theseembodiments of the invention. Furthermore, the Stein-Rosenberg theoremalso enables some physical intuition about the convergence. Roughlyspeaking, Gauss-Seidel iteration converges as long as the signal flowgraph for the original analog system does not contain a closed-loop gainlarger than 1. It is again worth mentioning that, because of bothtop-down design methodology and macromodeling technique, signal flows inan analog/RF system almost always propagate in a unique direction andfeedback signal paths are very weak. The above convergence analysisprovides the theoretical background to explain why the relaxationapproach works well for such cases.

It should be apparent that one way to implement the methods of theembodiments of the invention described herein is via computer programinstructions running on an appropriate computing platform. FIG. 8illustrates such a platform. Instruction execution system, 800, that isimplementing at least a portion of the invention. System bus 801interconnects the major components. The system is controlled byprocessor 802. System memory 803 is typically divided into variousregions or types or memory. At least one of those contains some of thecomputer program code instructions 804 which implement at least portionsof the invention. A plurality of input/output (I/O) adapters or devices,806, are present. These connect to various peripheral devices includingfixed, disk drive 807, optical drive 808, display 809, and keyboard 810.One adapter would also typically connect to a network. Computer programcode instructions which implement at least some of the functions of theinvention can be stored on fixed disk drive 807 as shown by block 812. Acomputer program product which contains instructions can also besupplied on a media, for example, optical disk 814.

Elements of the invention in fact may be embodied in hardware orsoftware. For example, in addition to taking the form of a computerprogram product on a medium, the computer program code can be stored inan electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor device. Additionally, the computer program may simply be astream of information being retrieved or downloaded through a networksuch as the Internet.

As a specific example of the application of an embodiment of theinvention, consider the block diagram of a GSM (Global System forMobile) receiver system, 900, implemented in 0.25 μm TSMC (TaiwanSemiconductor Manufacturing Company) CMOS process is shown in FIG. 9. Asimulator according to embodiments of the invention has actually beenapplied to the receiver system 900 of FIG. 9. The receiver includes anLNA, 902, an RF mixer, 904, and an IF amplifier, 906. Signal paths areindicated with arrows and frequencies are indicated with appropriatelegends. Since traditional algorithms cannot efficiently simulate largesystems, using a full GSM receiver would preclude comparison of thesimulator of the invention with traditional circuit simulationtechniques. However, since the relaxation approach partitions a largeproblem into a number of small ones, the relative efficiency of therelaxation simulator disclosed herein will be more pronounced as thesystem size increases.

A macromodel was created for each circuit block containing forwardsignal paths, as well as backward signal paths due to the nonidealcouplings. In this example, the reverse gain for a circuit block wasaround 20 dB-40 dB less than the forward gain at the center frequency.Next, based on these pre-characterized macromodels, the frequencyrelaxation simulator was used to simulate the entire system with variousinput excitations. All the simulations can be performed on a computingplatform such as a Sun Sparc™-450 MHz server.

If the input excitation is a single-tone sinusoidal signal in thesimulation, due to the nonlinearities, many harmonic components aregenerated. For comparison, the same macromodel for the GSM receiver wasdescribed by Verilog-A and run through a more traditional periodicsteady-state (PSS) analysis in SpectreRF with the same error tolerance.

This comparison reveals that the number of iterations required by therelaxation method is more than that by the Newton method in SpectreRF.However, since each relaxation iteration partitions the large systeminto much smaller units and thereby reduces the computation costsignificantly, the relaxation simulator of the invention eventuallyachieved about 1-2 orders of magnitude of runtime improvement.

In another comparison, a QAM-16 modulated signal was applied to theinput port of the GSM receiver. The carrier frequency is 923 MHz. First,the GSM receiver was described by means of a signal flow graph in MatlabSimulink™ and the system was simulated in the time domain. An FFT wasapplied to the time-domain output waveform. The overall computation timewas 73.38 seconds for such a time-domain simulation approach.

If a frequency-domain simulation is run directly with a relaxationsimulator according to an embodiment of the invention, the QAM-16modulated signal is represented by its continuous frequency spectrumwith 400 sampling points in the frequency domain. After runningcontinuous frequency spectra simulation for 6.09 seconds, the outputfrequency spectrum of the GSM receiver system was obtained. In such acomparison, the simulation results from both approaches are nearlyidentical, while a runtime improvement of more than 11× was achieved bythe relaxation simulator of the invention.

Finally, in a noise analysis for the entire GSM receiver system, thenoise macromodels for each circuit were extracted by the algorithmdiscussed herein. After that, cyclostationary noise sources were addedat the output port of each circuit block.

During noise simulation, the relaxation simulator first computestime-varying transfer functions from each input noise source to systemoutput. In this example, a time-varying transfer function H(t,jω) isrepresented by a set of harmonic transfer functions up to 7th order,i.e. {H_(n)(jω); n=0,±1, . . . ,±7}. For relaxation iteration, eachH_(n)(jω) is approximated by a piecewise-linear representation with 1000sampling points in the frequency domain. Next, the output noise valuewas evaluated based on the harmonic power spectral densities of inputnoise sources and the computed time-varying transfer functions from eachinput to output. The overall computation time for noise analysis was247.78 seconds. Note that, such cyclostationary noise analyses based onmacromodels are impractical for existing system-level simulationenvironments. Therefore, comparison with other methods for the exampleembodiment using a noise signal is difficult.

Specific embodiments of an invention have been herein described. One ofordinary skill in the circuit design arts will quickly recognize thatthe invention has numerous other embodiments. The following claims arein no way intended to limit the scope of the invention to the specificembodiments described.

1. A method of operating a simulator to determine an output response ofan analog system, the method comprising: partitioning the analog systeminto a plurality of blocks, at least one of the plurality of blocksbeing non-linear; for each block of the plurality of blocks, iterativelysimulating propagation of a frequency domain representation of anon-periodic input signal through the block to produce at least oneindividual non-periodic response for the block until a convergence pointhas been reached for the at least one individual non-periodic responsefor the block; computing the output response based on the individualnon-periodic responses for the plurality of blocks; and storing theoutput response at least temporarily.
 2. The method of claim 1 furthercomprising obtaining a block-level macromodel of the analog system,wherein the partitioning of the analog system is based on theblock-level macromodel.
 3. The method of claim 2 further comprisingrepresenting a total contribution of all noise sources in the analogsystem by injecting noise into the block-level macromodel.
 4. The methodof claim 2 wherein the non-periodic input signal comprises anon-periodic multi-tone sinusoidal signal.
 5. The method of claim 2wherein the non-periodic input signal comprises a non-periodiccontinuous spectra signal.
 6. The method of claim 2 wherein thenon-periodic input signal comprises a non-periodic stochastic signal. 7.The method of claim 1 wherein the non-periodic input signal comprises anon-periodic multi-tone sinusoidal signal.
 8. The method of claim 1wherein the non-periodic input signal comprises a non-periodiccontinuous spectra signal.
 9. The method of claim 1 wherein thenon-periodic input signal comprises a non-periodic stochastic signal.10. The method of claim 1 wherein the plurality of blocks comprise atime-varying block, and the at least one individual non-periodicresponse for the time-varying block is time-varying.
 11. A computerreadable storage medium storing a computer program comprising computerexecutable instructions for instructing a computing system to: partitionan analog system into a plurality of blocks, at least one of theplurality of blocks being non-linear; for each block of the plurality ofblocks, iteratively simulate propagation of a frequency domainrepresentation of a non-periodic input signal through the block toproduce at least one individual non-periodic response for the blockuntil a convergence point has been reached for the at least oneindividual non-periodic response for the block; compute an outputresponse of the analog system based on the individual non-periodicresponses for the plurality of blocks; and store the output response atleast temporarily.
 12. The computer readable storage medium of claim 11wherein the computer executable instructions further instruct thecomputing system to obtain a block-level macromodel of the analogsystem.
 13. The computer readable storage medium of claim 12 wherein thecomputer executable instructions further instruct the computing systemto represent a total contribution of all noise sources in the analogsystem by injecting noise into the block-level macromodel.
 14. Thecomputer readable storage medium of claim 13 wherein the non-periodicinput signal comprises at least one of a non-periodic multi-tonesinusoidal signal, a non-periodic continuous spectra signal, and anon-periodic stochastic signal.
 15. The computer readable storage mediumof claim 12 wherein the non-periodic input signal comprises at least oneof a non-periodic multi-tone sinusoidal signal, a non-periodiccontinuous spectra signal, and a non-periodic stochastic signal.
 16. Thecomputer readable storage medium of claim 11 wherein the non-periodicinput signal comprises at least one of a non-periodic multi-tonesinusoidal signal, a non-periodic continuous spectra signal, and anon-periodic stochastic signal.
 17. The computer readable storage mediumof claim 11 wherein the plurality of blocks comprise a time-varyingblock, and the at least one individual non-periodic response for thetime-varying block is time-varying.
 18. An apparatus for determining anoutput response of an analog system, the apparatus comprising: means forpartitioning the analog system into a plurality of blocks, at least oneof the plurality of blocks being non-linear; for each block of theplurality of blocks, means for iteratively simulating propagation of afrequency domain representation of a non-periodic input signal throughthe block to produce at least one individual non-periodic response forthe block until a convergence point has been reached for the at leastone individual non-periodic response for the block; means for computingthe output response based on the individual non-periodic responses forthe plurality of blocks; and means for storing the output response atleast temporarily.
 19. The apparatus of claim 18 further comprisingmeans for obtaining a block-level macromodel of the analog system. 20.The apparatus of claim 19 further comprising means for representing atotal contribution of all noise sources in the analog system byinjecting noise into the block-level macromodel.
 21. The apparatus ofclaim 20 wherein the non-periodic input signal comprises at least one ofa non-periodic multi-tone sinusoidal signal, a non-periodic continuousspectra signal, and a non-periodic stochastic signal.
 22. The apparatusof claim 19 wherein the non-periodic input signal comprises at least oneof a non-periodic multi-tone sinusoidal signal, a non-periodiccontinuous spectra signal, and a non-periodic stochastic signal.
 23. Theapparatus of claim 18 wherein the non-periodic input signal comprises atleast one of a non-periodic multi-tone sinusoidal signal, a non-periodiccontinuous spectra signal, and a non-periodic stochastic signal.
 24. Theapparatus of claim 18 wherein the plurality of blocks comprise atime-varying block, and the at least one individual non-periodicresponse for the time-varying block is time-varying.